While once used almost exclusively for video and image rendering, graphics processing units – GPUs – are now being put to the test in the three fastest developing applications in today’s tech ecosystem: big data analytics, machine learning, and generative AI. But as demand for these GPUs skyrockets, supply is having a hard time keeping up: Recent indicate these chips are anywhere between 2-3 months behind fulfillment. reports As is the case with any sort of tech integration, there are better ways to maximize the power of GPUs. Rethinking these protocols can not only mitigate the exorbitant demand for GPUs, but also alleviate many of the pressures the GPU industry is currently facing from the burgeoning generative AI revolution. Data Scientists’ Toolbox In order to appreciate the extent to which GPUs enhance machine learning, it’s important to first understand the analysis tools data scientists use to manipulate and prepare data: Pandas and SQLAlchemy. Built on top of the Python’s NumPy library, Pandas is an open-source library that provides transformative data manipulation functions such as filtering, grouping, merging, and reshaping, enabling data scientists to execute complex mathematical tasks. SQLAlchemy, on the other hand, is an SQL toolkit and Object-Relational Mapping (ORM) library within Python programming that abstracts low-level details of connecting to relational databases, allowing data scientists to interact with them via more easily. Moreover, leveraging SQLAlchemy can facilitate data migrations, schema management, as well as help build applications backed by databases. CRUD operations The Data Problem Aside from training and inference procedures, Pandas and SQLAlchemy comprise the backbone of machine learning. But once enterprises have amassed data, they must begin the tedious task that is data preparation – the formatting of raw data, often terabytes and petabytes of data – into a form appropriate for machine learning algorithms. Preparing such substantial volumes of data, albeit valuable for improving accuracy and generalization in machine learning models, is a difficult and time-consuming task requiring considerable effort from both data scientists and machine learning platforms – not to mention the expensive processing costs clients have to pay for. Although current Python libraries can help manipulate preprocessed data and accelerate preparation, data governance is often siloed, decentralized, and inconsistent, further burdening the process. That is why the operational pain points and business insight bottlenecks enterprises regularly experience vis a vis their data require innovative solutions. Maximizing GPUs is one such solution that can ameliorate both. GPUs With a Twist Between their incredible parallelism capabilities, processing speeds, and increasingly affordable hardware costs, the value GPUs deliver to enterprises is growing thanks to advanced methods of utilization. Indeed, in the last ten years, companies have been able to leverage greater computational power having to allocate significant funds towards upgraded software or talent acquisition. without But due to supply shortages, prohibitive energy requirements, and inequitable access to GPUs, capitalizing on these advantages at the moment is . easier said than done Remediating this issue will require data scientists to optimize their use of GPUs such that they can still power AI applications propel the industry into its next phase, prompting cumbersome delays and incurring massive investment. and without One option is to run training jobs on their own database, which would allow users to eliminate the need to decouple the processing engine from the training process, resulting in much faster training times and performance outputs. Embrace Creativity Despite from the public surrounding AI, big data analytics, machine learning, and generative AI are set for tremendous growth. With the horses out of the shed, it’s imperative that data scientists find the most efficient and cost-effective way to leverage these technologies. concerns GPUs offer the most promising path forward – they expedite data preparation tasks, facilitate complex transformations and aggregations, and yield quicker model execution on the entire end to end process. But because market forces surrounding them remain turbulent, data scientists will need to take creative steps to maximize the GPUs they already have if they wish to unlock the next generation of business insights.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

The Added Value of GPU-Accelerated Analytics

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Inon Maman

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